Long string pattern matching of aggregated account data

#In modern financial institutions, data management and utilization is paramount for providing the best possible service to clients or customers while increasing the profitability of the financial institution. Such things as purchases, automatic payments and deposits, transfers and other repeated transactions have been facilitated by financial entities, payees, and employers for the convenience of all involved.

#A benefit for financial service providers is that by facilitating such transactions they increase the number of services that they can provide to their customers. The increased interaction is created in part because setting up automatic transactions and discovering new transactions that can be done automatically is time consuming and difficult. Accordingly, many customers and patrons are deterred from change because of the difficulty in changing automatic services. Additionally, better financial institutions are deterred from attracting new customers, or providing new services, because many of the customers have existing automatic services with lesser service providers.

#The disclosure relates to a method and system to use indexable data strings that match on patterns and allow lookup of matches to take significantly less time and even avoid disk access with the addition of presence (bloom) filters to determine presence of a match in the dataset before referencing the index at all. This reduces the seek time for a unique pattern in a large dataset to at best O(1) and worst O(log(n)). This allows the matching of large datasets against other large datasets based on patterns and this can save a significant amount of processing time and needed hardware/software for discovering transaction data.

#Non-limiting and non-exhaustive implementations of the disclosure are described with reference to the following figures, wherein like reference numerals refer to like parts throughout the various views unless otherwise specified. Advantages of the disclosure will become better understood with regard to the following description and accompanying drawings where:

#FIG. 1 illustrates a computer environment and network for identifying transaction within aggregated data in accordance with the principles and teachings of the disclosure;

#FIG. 2 illustrates a schematic representation of computer hardware and protocols that enable the various embodiments discussed herein in accordance with the principles and teachings of the disclosure;

#The disclosure extends to methods, systems, and computer based products for discovering transaction attributes within a database of aggregated account data including, aggregating and matching financial account attributes between a plurality of accounts in order to provide additional services to account holders. In the following description of the disclosure, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific implementations in which the disclosure may be practiced. It is understood that other implementations may be utilized and structural changes may be made without departing from the scope of the disclosure.

#Aggregated account data generally can provide historical transaction information once it is parsed through by a knowledgeable entity. Everyday transactions that are repeated provide opportunity and information for financial service providers, retailers and customers alike. Such transactions as regular payments (bill-pay) and deposits (direct deposit) are important services that financial institutions provide to their customers and may be automated, or suggested for automation, for the convenience of customers once discovered in the account data. For simplicity and clarity, the service of making bill payments will be primarily used in the embodiments of the disclosure, however, the advantages and features disclosed herein also apply equally to any repeatable transaction or identifiable transaction from within aggregated data. Such services are so important in fact that customers will often select a financial institution and stay with a financial institution due to the automated services like bill-pay over many other factors. A common problem related to suggesting and providing additional services, such as to customers, is that the current transactions of interest may be with data that is stored by third parties and must aggregated before it can be used. Additionally once the data has been aggregated, this large amount of data must be made usable. Accordingly, systems and methods will be disclosed herein for automating the process of identifying transaction from within large aggregation data stores.

#In describing and claiming the subject matter of the disclosure, the following terminology will be used in accordance with the definitions set out below.